In recent years, several companies and researchers have started to tackle the problem of damage recognition within the scope of automated inspection of built structures. While companies are neither willing to publish associated data nor models, researchers are facing the problem of data shortage on one hand and inconsistent dataset splitting with the absence of consistent metrics on the other hand. This leads to incomparable results. Therefore, we introduce the building inspection toolkit -- bikit -- which acts as a simple to use data hub containing relevant open-source datasets in the field of damage recognition. The datasets are enriched with evaluation splits and predefined metrics, suiting the specific task and their data distribution. For the sake of compatibility and to motivate researchers in this domain, we also provide a leaderboard and the possibility to share model weights with the community. As starting point we provide strong baselines for multi-target classification tasks utilizing extensive hyperparameter search using three transfer learning approaches for state-of-the-art algorithms. The toolkit and the leaderboard are available online.

翻译：近年来,若干公司和研究人员开始在对建筑结构进行自动检查的范围内解决损害确认问题,虽然公司不愿意公布相关数据和模型,但研究人员正面临数据短缺和数据元数不一致的问题,而缺乏一致的衡量标准则与此脱钩。这导致无法比较的结果。因此,我们引入了建筑检验工具包(bikit),作为使用包含损害确认领域相关公开源数据集的数据枢纽的简单工具。数据集通过评估分解和预先界定的计量方法得到丰富,适合具体任务及其数据分布。为了兼容性和激励这一领域的研究人员,我们还提供了一个领导板,并提供了与社区分享模型权重的可能性。作为起点,我们利用对最新算法的三种传输学习方法进行广泛的超参数搜索,为多目标分类任务提供了强有力的基线。工具包和头板可以在线查阅。